标题： 基于采样的有向复杂网络入度分布估计方法及其应用 显示英文标题

标题： Sampling-based Estimation of In-degree Distribution with Applications to Directed Complex Networks

摘要： 本文工作的重点是从采样的网络节点或边来估计有向网络中的入度分布。考虑了多种抽样方案，包括带放回和不带放回的随机抽样，以及几种基于可能跳跃的随机游走的方法。当抽样节点时，假设只能看到该节点的出边，即该节点的入度未被观测到。建议的入度分布估计方法基于两种方法。反转方法利用原始入度分布和样本入度分布之间的关系，可以估计入度分布的主体部分，但不能估计分布的尾部。通过渐近方法来估计入度分布的尾部，该方法本身有两种版本：一种假设幂律尾部，另一种适用于一般形式的尾部。这两种估计方法在合成网络和真实网络上进行了检验，结果表现良好，特别是渐近方法的表现尤为突出。 显示英文摘要

摘要： The focus of this work is on estimation of the in-degree distribution in directed networks from sampling network nodes or edges. A number of sampling schemes are considered, including random sampling with and without replacement, and several approaches based on random walks with possible jumps. When sampling nodes, it is assumed that only the out-edges of that node are visible, that is, the in-degree of that node is not observed. The suggested estimation of the in-degree distribution is based on two approaches. The inversion approach exploits the relation between the original and sample in-degree distributions, and can estimate the bulk of the in-degree distribution, but not the tail of the distribution. The tail of the in-degree distribution is estimated through an asymptotic approach, which itself has two versions: one assuming a power-law tail and the other for a tail of general form. The two estimation approaches are examined on synthetic and real networks, with good performance results, especially striking for the asymptotic approach.